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AMD Ryzen 7 7840U testing with a Framework FRANMDCP07 (03.03 BIOS) and AMD Phoenix1 512MB on Ubuntu 23.10 via the Phoronix Test Suite.

Compare your own system(s) to this result file with the Phoronix Test Suite by running the command: phoronix-test-suite benchmark 2312167-NE-AI239537207
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Result
Identifier
Performance Per
Dollar
Date
Run
  Test
  Duration
Ryzen 7 7840U
December 16 2023
  6 Hours, 42 Minutes
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Ryzen 7 7840U

Processor: AMD Ryzen 7 7840U @ 5.13GHz (8 Cores / 16 Threads), Motherboard: Framework FRANMDCP07 (03.03 BIOS), Chipset: AMD Device 14e8, Memory: 16GB, Disk: 512GB Western Digital WD PC SN740 SDDPNQD-512G, Graphics: AMD Phoenix1 512MB (2700/2800MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: MEDIATEK MT7922 802.11ax PCI

OS: Ubuntu 23.10, Kernel: 6.7.0-060700rc5-generic (x86_64), Desktop: GNOME Shell 45.1, Display Server: X Server 1.21.1.7 + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 DRM 3.56), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 2256x1504

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: amd-pstate-epp powersave (EPP: performance) - Platform Profile: balanced - CPU Microcode: 0xa704103 - ACPI Profile: balanced
Python Notes: Python 3.11.6
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Vulnerable: Safe RET no microcode + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

aiOpenBenchmarking.orgPhoronix Test SuiteAMD Ryzen 7 7840U @ 5.13GHz (8 Cores / 16 Threads)Framework FRANMDCP07 (03.03 BIOS)AMD Device 14e816GB512GB Western Digital WD PC SN740 SDDPNQD-512GAMD Phoenix1 512MB (2700/2800MHz)AMD Rembrandt Radeon HD AudioMEDIATEK MT7922 802.11ax PCIUbuntu 23.106.7.0-060700rc5-generic (x86_64)GNOME Shell 45.1X Server 1.21.1.7 + Wayland4.6 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 DRM 3.56)GCC 13.2.0ext42256x1504ProcessorMotherboardChipsetMemoryDiskGraphicsAudioNetworkOSKernelDesktopDisplay ServerOpenGLCompilerFile-SystemScreen ResolutionAi BenchmarksSystem Logs- Transparent Huge Pages: madvise- --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v - Scaling Governor: amd-pstate-epp powersave (EPP: performance) - Platform Profile: balanced - CPU Microcode: 0xa704103 - ACPI Profile: balanced - Python 3.11.6- gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Vulnerable: Safe RET no microcode + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

aiscikit-learn: Isolation Forestncnn: CPU - FastestDetncnn: CPU - vision_transformerncnn: CPU - regnety_400mncnn: CPU - squeezenet_ssdncnn: CPU - yolov4-tinyncnn: CPU - resnet50ncnn: CPU - alexnetncnn: CPU - resnet18ncnn: CPU - vgg16ncnn: CPU - googlenetncnn: CPU - blazefacencnn: CPU - efficientnet-b0ncnn: CPU - mnasnetncnn: CPU - shufflenet-v2ncnn: CPU-v3-v3 - mobilenet-v3ncnn: CPU-v2-v2 - mobilenet-v2ncnn: CPU - mobilenetscikit-learn: TSNE MNIST Datasetnumenta-nab: KNN CADopencv: DNN - Deep Neural Networkopencv: Object Detectionscikit-learn: SGD Regressionnumenta-nab: Bayesian Changepointnumenta-nab: Earthgecko Skylinestargate: 192000 - 1024opencv: Corescikit-learn: Hist Gradient Boostingscikit-learn: MNIST Datasetpyhpc: CPU - Numpy - 4194304 - Isoneutral Mixingstargate: 96000 - 1024scikit-learn: Hist Gradient Boosting Adultscikit-learn: 20 Newsgroups / Logistic Regressionstargate: 480000 - 1024stargate: 44100 - 1024numenta-nab: Contextual Anomaly Detector OSEpyhpc: CPU - Numpy - 4194304 - Equation of Staternnoise: numenta-nab: Relative Entropynumenta-nab: Windowed Gaussianai-benchmark: Ryzen 7 7840U223.4742.9266.986.057.8717.2113.205.735.8641.128.270.794.002.562.292.682.9910.54219.823234.008364873435596.58425.36689.0491.5067669941471.12255.6281.4522.28246649.65334.8433.0903163.12355235.2280.98915.13911.6776.954OpenBenchmarking.org

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Isolation ForestRyzen 7 7840U50100150200250SE +/- 0.47, N = 3223.471. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.817.621.1OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor612182430

NCNN

NCNN is a high performance neural network inference framework optimized for mobile and other platforms developed by Tencent. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: FastestDetRyzen 7 7840U0.6571.3141.9712.6283.285SE +/- 0.03, N = 142.92MIN: 2.53 / MAX: 14.811. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread
MinAvgMaxRyzen 7 7840U5.031.739.5OpenBenchmarking.orgWatts, Fewer Is BetterNCNN 20230517CPU Power Consumption Monitor1122334455

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vision_transformerRyzen 7 7840U1530456075SE +/- 0.06, N = 1566.98MIN: 63.99 / MAX: 105.51. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: regnety_400mRyzen 7 7840U246810SE +/- 0.03, N = 156.05MIN: 5.45 / MAX: 18.141. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: squeezenet_ssdRyzen 7 7840U246810SE +/- 0.13, N = 157.87MIN: 6.63 / MAX: 40.311. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: yolov4-tinyRyzen 7 7840U48121620SE +/- 0.18, N = 1517.21MIN: 15.85 / MAX: 32.091. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet50Ryzen 7 7840U3691215SE +/- 0.19, N = 1513.20MIN: 10.88 / MAX: 39.041. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: alexnetRyzen 7 7840U1.28932.57863.86795.15726.4465SE +/- 0.07, N = 155.73MIN: 4.95 / MAX: 17.881. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: resnet18Ryzen 7 7840U1.31852.6373.95555.2746.5925SE +/- 0.10, N = 155.86MIN: 5.14 / MAX: 18.931. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: vgg16Ryzen 7 7840U918273645SE +/- 0.18, N = 1541.12MIN: 38.67 / MAX: 68.321. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: googlenetRyzen 7 7840U246810SE +/- 0.14, N = 158.27MIN: 7.08 / MAX: 53.051. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: blazefaceRyzen 7 7840U0.17780.35560.53340.71120.889SE +/- 0.01, N = 150.79MIN: 0.74 / MAX: 2.881. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: efficientnet-b0Ryzen 7 7840U0.91.82.73.64.5SE +/- 0.02, N = 154.00MIN: 3.55 / MAX: 15.961. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mnasnetRyzen 7 7840U0.5761.1521.7282.3042.88SE +/- 0.02, N = 152.56MIN: 2.27 / MAX: 15.421. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: shufflenet-v2Ryzen 7 7840U0.51531.03061.54592.06122.5765SE +/- 0.01, N = 152.29MIN: 2.07 / MAX: 27.161. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v3-v3 - Model: mobilenet-v3Ryzen 7 7840U0.6031.2061.8092.4123.015SE +/- 0.01, N = 152.68MIN: 2.38 / MAX: 13.631. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU-v2-v2 - Model: mobilenet-v2Ryzen 7 7840U0.67281.34562.01842.69123.364SE +/- 0.01, N = 152.99MIN: 2.59 / MAX: 14.021. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

OpenBenchmarking.orgms, Fewer Is BetterNCNN 20230517Target: CPU - Model: mobilenetRyzen 7 7840U3691215SE +/- 0.08, N = 1510.54MIN: 9.52 / MAX: 23.751. (CXX) g++ options: -O3 -rdynamic -lgomp -lpthread

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: TSNE MNIST DatasetRyzen 7 7840U50100150200250SE +/- 0.32, N = 3219.821. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.732.750.9OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor1428425670

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: KNN CADRyzen 7 7840U50100150200250SE +/- 2.15, N = 3234.01
MinAvgMaxRyzen 7 7840U6.733.056.6OpenBenchmarking.orgWatts, Fewer Is BetterNumenta Anomaly Benchmark 1.1CPU Power Consumption Monitor1632486480

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenCV 4.7Test: DNN - Deep Neural NetworkRyzen 7 7840U8K16K24K32K40KSE +/- 282.92, N = 15364871. (CXX) g++ options: -fPIC -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -shared
MinAvgMaxRyzen 7 7840U6.628.350.1OpenBenchmarking.orgWatts, Fewer Is BetterOpenCV 4.7CPU Power Consumption Monitor1428425670

OpenBenchmarking.orgms, Fewer Is BetterOpenCV 4.7Test: Object DetectionRyzen 7 7840U7K14K21K28K35KSE +/- 560.55, N = 12343551. (CXX) g++ options: -fPIC -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -shared
MinAvgMaxRyzen 7 7840U6.824.638.8OpenBenchmarking.orgWatts, Fewer Is BetterOpenCV 4.7CPU Power Consumption Monitor1122334455

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: SGD RegressionRyzen 7 7840U20406080100SE +/- 0.05, N = 396.581. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.820.643.7OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor1224364860

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Bayesian ChangepointRyzen 7 7840U612182430SE +/- 0.25, N = 1525.37
MinAvgMaxRyzen 7 7840U6.729.851.1OpenBenchmarking.orgWatts, Fewer Is BetterNumenta Anomaly Benchmark 1.1CPU Power Consumption Monitor1530456075

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Earthgecko SkylineRyzen 7 7840U20406080100SE +/- 1.10, N = 489.05
MinAvgMaxRyzen 7 7840U6.832.151.0OpenBenchmarking.orgWatts, Fewer Is BetterNumenta Anomaly Benchmark 1.1CPU Power Consumption Monitor1530456075

Stargate Digital Audio Workstation

Stargate is an open-source, cross-platform digital audio workstation (DAW) software package with "a unique and carefully curated experience" with scalability from old systems up through modern multi-core systems. Stargate is GPLv3 licensed and makes use of Qt5 (PyQt5) for its user-interface. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRender Ratio, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 192000 - Buffer Size: 1024Ryzen 7 7840U0.3390.6781.0171.3561.695SE +/- 0.007666, N = 31.5067661. (CXX) g++ options: -lpthread -lsndfile -lm -O3 -march=native -ffast-math -funroll-loops -fstrength-reduce -fstrict-aliasing -finline-functions
OpenBenchmarking.orgRender Ratio Per Watt, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 192000 - Buffer Size: 1024Ryzen 7 7840U0.01080.02160.03240.04320.0540.048
MinAvgMaxRyzen 7 7840U6.731.643.3OpenBenchmarking.orgWatts, Fewer Is BetterStargate Digital Audio Workstation 22.11.5CPU Power Consumption Monitor1224364860

OpenCV

This is a benchmark of the OpenCV (Computer Vision) library's built-in performance tests. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgms, Fewer Is BetterOpenCV 4.7Test: CoreRyzen 7 7840U20K40K60K80K100KSE +/- 290.67, N = 3994141. (CXX) g++ options: -fPIC -fsigned-char -pthread -fomit-frame-pointer -ffunction-sections -fdata-sections -msse -msse2 -msse3 -fvisibility=hidden -O3 -shared
MinAvgMaxRyzen 7 7840U6.619.150.0OpenBenchmarking.orgWatts, Fewer Is BetterOpenCV 4.7CPU Power Consumption Monitor1428425670

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient BoostingRyzen 7 7840U1632486480SE +/- 0.43, N = 371.121. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.831.237.0OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor1122334455

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: MNIST DatasetRyzen 7 7840U1224364860SE +/- 0.04, N = 355.631. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.316.019.3OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor612182430

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Isoneutral MixingRyzen 7 7840U0.32670.65340.98011.30681.6335SE +/- 0.005, N = 31.452
MinAvgMaxRyzen 7 7840U6.818.720.8OpenBenchmarking.orgWatts, Fewer Is BetterPyHPC Benchmarks 3.0CPU Power Consumption Monitor612182430

Stargate Digital Audio Workstation

Stargate is an open-source, cross-platform digital audio workstation (DAW) software package with "a unique and carefully curated experience" with scalability from old systems up through modern multi-core systems. Stargate is GPLv3 licensed and makes use of Qt5 (PyQt5) for its user-interface. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRender Ratio, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 96000 - Buffer Size: 1024Ryzen 7 7840U0.51361.02721.54082.05442.568SE +/- 0.010681, N = 32.2824661. (CXX) g++ options: -lpthread -lsndfile -lm -O3 -march=native -ffast-math -funroll-loops -fstrength-reduce -fstrict-aliasing -finline-functions
OpenBenchmarking.orgRender Ratio Per Watt, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 96000 - Buffer Size: 1024Ryzen 7 7840U0.01640.03280.04920.06560.0820.073
MinAvgMaxRyzen 7 7840U6.631.244.0OpenBenchmarking.orgWatts, Fewer Is BetterStargate Digital Audio Workstation 22.11.5CPU Power Consumption Monitor1224364860

Scikit-Learn

Scikit-learn is a Python module for machine learning built on NumPy, SciPy, and is BSD-licensed. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: Hist Gradient Boosting AdultRyzen 7 7840U1122334455SE +/- 0.17, N = 349.651. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.830.033.6OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor1020304050

OpenBenchmarking.orgSeconds, Fewer Is BetterScikit-Learn 1.2.2Benchmark: 20 Newsgroups / Logistic RegressionRyzen 7 7840U816243240SE +/- 0.06, N = 334.841. (F9X) gfortran options: -O0
MinAvgMaxRyzen 7 7840U6.928.447.7OpenBenchmarking.orgWatts, Fewer Is BetterScikit-Learn 1.2.2CPU Power Consumption Monitor1428425670

Stargate Digital Audio Workstation

Stargate is an open-source, cross-platform digital audio workstation (DAW) software package with "a unique and carefully curated experience" with scalability from old systems up through modern multi-core systems. Stargate is GPLv3 licensed and makes use of Qt5 (PyQt5) for its user-interface. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgRender Ratio, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 480000 - Buffer Size: 1024Ryzen 7 7840U0.69531.39062.08592.78123.4765SE +/- 0.012252, N = 33.0903161. (CXX) g++ options: -lpthread -lsndfile -lm -O3 -march=native -ffast-math -funroll-loops -fstrength-reduce -fstrict-aliasing -finline-functions
OpenBenchmarking.orgRender Ratio Per Watt, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 480000 - Buffer Size: 1024Ryzen 7 7840U0.02230.04460.06690.08920.11150.099
MinAvgMaxRyzen 7 7840U6.631.449.2OpenBenchmarking.orgWatts, Fewer Is BetterStargate Digital Audio Workstation 22.11.5CPU Power Consumption Monitor1428425670

OpenBenchmarking.orgRender Ratio, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 44100 - Buffer Size: 1024Ryzen 7 7840U0.70281.40562.10842.81123.514SE +/- 0.032515, N = 33.1235521. (CXX) g++ options: -lpthread -lsndfile -lm -O3 -march=native -ffast-math -funroll-loops -fstrength-reduce -fstrict-aliasing -finline-functions
OpenBenchmarking.orgRender Ratio Per Watt, More Is BetterStargate Digital Audio Workstation 22.11.5Sample Rate: 44100 - Buffer Size: 1024Ryzen 7 7840U0.0230.0460.0690.0920.1150.102
MinAvgMaxRyzen 7 7840U6.630.751.0OpenBenchmarking.orgWatts, Fewer Is BetterStargate Digital Audio Workstation 22.11.5CPU Power Consumption Monitor1530456075

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Contextual Anomaly Detector OSERyzen 7 7840U816243240SE +/- 0.14, N = 335.23
MinAvgMaxRyzen 7 7840U7.023.551.0OpenBenchmarking.orgWatts, Fewer Is BetterNumenta Anomaly Benchmark 1.1CPU Power Consumption Monitor1530456075

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterPyHPC Benchmarks 3.0Device: CPU - Backend: Numpy - Project Size: 4194304 - Benchmark: Equation of StateRyzen 7 7840U0.22250.4450.66750.891.1125SE +/- 0.003, N = 30.989
MinAvgMaxRyzen 7 7840U6.419.922.9OpenBenchmarking.orgWatts, Fewer Is BetterPyHPC Benchmarks 3.0CPU Power Consumption Monitor612182430

RNNoise

RNNoise is a recurrent neural network for audio noise reduction developed by Mozilla and Xiph.Org. This test profile is a single-threaded test measuring the time to denoise a sample 26 minute long 16-bit RAW audio file using this recurrent neural network noise suppression library. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterRNNoise 2020-06-28Ryzen 7 7840U48121620SE +/- 0.05, N = 415.141. (CC) gcc options: -O2 -pedantic -fvisibility=hidden
MinAvgMaxRyzen 7 7840U6.714.516.9OpenBenchmarking.orgWatts, Fewer Is BetterRNNoise 2020-06-28CPU Power Consumption Monitor510152025

Numenta Anomaly Benchmark

Numenta Anomaly Benchmark (NAB) is a benchmark for evaluating algorithms for anomaly detection in streaming, real-time applications. It is comprised of over 50 labeled real-world and artificial time-series data files plus a novel scoring mechanism designed for real-time applications. This test profile currently measures the time to run various detectors. Learn more via the OpenBenchmarking.org test page.

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Relative EntropyRyzen 7 7840U3691215SE +/- 0.07, N = 411.68
MinAvgMaxRyzen 7 7840U6.728.651.2OpenBenchmarking.orgWatts, Fewer Is BetterNumenta Anomaly Benchmark 1.1CPU Power Consumption Monitor1530456075

OpenBenchmarking.orgSeconds, Fewer Is BetterNumenta Anomaly Benchmark 1.1Detector: Windowed GaussianRyzen 7 7840U246810SE +/- 0.047, N = 66.954
MinAvgMaxRyzen 7 7840U3.627.651.2OpenBenchmarking.orgWatts, Fewer Is BetterNumenta Anomaly Benchmark 1.1CPU Power Consumption Monitor1530456075

spaCy

The spaCy library is an open-source solution for advanced neural language processing (NLP). The spaCy library leverages Python and is a leading neural language processing solution. This test profile times the spaCy CPU performance with various models. Learn more via the OpenBenchmarking.org test page.

Ryzen 7 7840U: The test quit with a non-zero exit status. E: ValueError: 'in' is not a valid parameter name

PyHPC Benchmarks

PyHPC-Benchmarks is a suite of Python high performance computing benchmarks for execution on CPUs and GPUs using various popular Python HPC libraries. The PyHPC CPU-based benchmarks focus on sequential CPU performance. Learn more via the OpenBenchmarking.org test page.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Equation of State

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: TensorFlow - Project Size: 4194304 - Benchmark: Equation of State

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: PyTorch - Project Size: 4194304 - Benchmark: Equation of State

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: Aesara - Project Size: 4194304 - Benchmark: Equation of State

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: Numba - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Isoneutral Mixing

Ryzen 7 7840U: The test run did not produce a result.

Device: CPU - Backend: JAX - Project Size: 4194304 - Benchmark: Equation of State

Ryzen 7 7840U: The test run did not produce a result.

AI Benchmark Alpha

AI Benchmark Alpha is a Python library for evaluating artificial intelligence (AI) performance on diverse hardware platforms and relies upon the TensorFlow machine learning library. Learn more via the OpenBenchmarking.org test page.

Ryzen 7 7840U: The test quit with a non-zero exit status. E: ModuleNotFoundError: No module named 'tensorflow'

CPU Power Consumption Monitor

OpenBenchmarking.orgWattsCPU Power Consumption MonitorPhoronix Test Suite System MonitoringRyzen 7 7840U1122334455Min: 3.64 / Avg: 25.89 / Max: 56.62

Meta Performance Per Watts

OpenBenchmarking.orgPerformance Per Watts, More Is BetterMeta Performance Per WattsPerformance Per WattsRyzen 7 7840U0.54011.08021.62032.16042.70052.4004

Ryzen 7 7840U

Processor: AMD Ryzen 7 7840U @ 5.13GHz (8 Cores / 16 Threads), Motherboard: Framework FRANMDCP07 (03.03 BIOS), Chipset: AMD Device 14e8, Memory: 16GB, Disk: 512GB Western Digital WD PC SN740 SDDPNQD-512G, Graphics: AMD Phoenix1 512MB (2700/2800MHz), Audio: AMD Rembrandt Radeon HD Audio, Network: MEDIATEK MT7922 802.11ax PCI

OS: Ubuntu 23.10, Kernel: 6.7.0-060700rc5-generic (x86_64), Desktop: GNOME Shell 45.1, Display Server: X Server 1.21.1.7 + Wayland, OpenGL: 4.6 Mesa 23.2.1-1ubuntu3.1 (LLVM 15.0.7 DRM 3.56), Compiler: GCC 13.2.0, File-System: ext4, Screen Resolution: 2256x1504

Kernel Notes: Transparent Huge Pages: madvise
Compiler Notes: --build=x86_64-linux-gnu --disable-vtable-verify --disable-werror --enable-bootstrap --enable-cet --enable-checking=release --enable-clocale=gnu --enable-default-pie --enable-gnu-unique-object --enable-languages=c,ada,c++,go,d,fortran,objc,obj-c++,m2 --enable-libphobos-checking=release --enable-libstdcxx-debug --enable-libstdcxx-time=yes --enable-link-serialization=2 --enable-multiarch --enable-multilib --enable-nls --enable-objc-gc=auto --enable-offload-defaulted --enable-offload-targets=nvptx-none=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-nvptx/usr,amdgcn-amdhsa=/build/gcc-13-XYspKM/gcc-13-13.2.0/debian/tmp-gcn/usr --enable-plugin --enable-shared --enable-threads=posix --host=x86_64-linux-gnu --program-prefix=x86_64-linux-gnu- --target=x86_64-linux-gnu --with-abi=m64 --with-arch-32=i686 --with-build-config=bootstrap-lto-lean --with-default-libstdcxx-abi=new --with-gcc-major-version-only --with-multilib-list=m32,m64,mx32 --with-target-system-zlib=auto --with-tune=generic --without-cuda-driver -v
Processor Notes: Scaling Governor: amd-pstate-epp powersave (EPP: performance) - Platform Profile: balanced - CPU Microcode: 0xa704103 - ACPI Profile: balanced
Python Notes: Python 3.11.6
Security Notes: gather_data_sampling: Not affected + itlb_multihit: Not affected + l1tf: Not affected + mds: Not affected + meltdown: Not affected + mmio_stale_data: Not affected + retbleed: Not affected + spec_rstack_overflow: Vulnerable: Safe RET no microcode + spec_store_bypass: Mitigation of SSB disabled via prctl + spectre_v1: Mitigation of usercopy/swapgs barriers and __user pointer sanitization + spectre_v2: Mitigation of Enhanced / Automatic IBRS IBPB: conditional STIBP: always-on RSB filling PBRSB-eIBRS: Not affected + srbds: Not affected + tsx_async_abort: Not affected

Testing initiated at 16 December 2023 11:34 by user phoronix.